Analysis and Classification of Frequency-Domain Terahertz Reflection Spectra Using Supervised and Unsupervised Dimensionality Reduction Methods

Pawel Piotr Cielecki, Mathias Hedegaard Kristensen, Esben Skovsen*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

The unique properties of terahertz (THz) spectroscopy show a great potential for security and defense applications such as safe screening of persons and objects. However, a successful implementation of THz screening systems requires a development of reliable and efficient identification algorithms. Dimensionality reduction (DR) methods aim to reduce the dimensionality of the multivariate data and are therefore commonly used as a preprocessing step for classification algorithms and as an analytical tool allowing data visualization. In this paper, we compare the use of unsupervised and supervised DR methods for analysis and classification of THz reflection spectra based on their most widespread linear representatives, namely principal component analysis and linear discriminant analysis, respectively. To this end, both methods were applied to more than 5000 THz reflection spectra acquired from six active materials mixed at three different concentrations with polyethylene and measured at various humidity conditions. While considering scenarios with different levels of complexity, we found that the supervised approach provides better results because it enables efficient grouping despite intra-class variability. Furthermore, we showed that manipulating labels introduced into the supervised DR algorithm allows conditioning the data for a desired classification task such as security screening. Presented classification results show that simple machine learning algorithms are sufficient for highly accurate classification (>98.6%) of THz spectra, which will be suitable for many real-life applications of THz spectroscopy based on material identification.

Original languageEnglish
JournalJournal of Infrared, Millimeter and Terahertz Waves
Volume42
Issue number9-10
Pages (from-to)1005-1026
Number of pages22
ISSN1866-6892
DOIs
Publication statusPublished - Sep 2021

Keywords

  • Classification
  • Dimensionality reduction
  • Frequency-domain
  • Machine learning
  • THz screening
  • THz spectroscopy

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